/*
 * Licensed to the Apache Software Foundation (ASF) under one or more
 * contributor license agreements.  See the NOTICE file distributed with
 * this work for additional information regarding copyright ownership.
 * The ASF licenses this file to You under the Apache License, Version 2.0
 * (the "License"); you may not use this file except in compliance with
 * the License.  You may obtain a copy of the License at
 *
 *    http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 */

package org.apache.spark.sql.execution.joins

import org.apache.spark.annotation.DeveloperApi
import org.apache.spark.rdd.RDD
import org.apache.spark.sql.Row
import org.apache.spark.sql.catalyst.expressions.{Attribute, JoinedRow}
import org.apache.spark.sql.execution.{BinaryNode, SparkPlan}

/**
 * :: DeveloperApi ::
 */
@DeveloperApi
case class CartesianProduct(left: SparkPlan, right: SparkPlan) extends BinaryNode {
  override def output: Seq[Attribute] = left.output ++ right.output

  override def execute(): RDD[Row] = {
    val leftResults = left.execute().map(_.copy())
    val rightResults = right.execute().map(_.copy())

    leftResults.cartesian(rightResults).mapPartitions { iter =>
      val joinedRow = new JoinedRow
      iter.map(r => joinedRow(r._1, r._2))
    }
  }
}
